Relationship Between Performance on Part I of the American Board of Orthopaedic Surgery Certifying Examination and Scores on USMLE Steps 1 and 2
Bibliographic record
Abstract
BACKGROUND: This study investigated the strength of the relationship between performance on Part I of the American Board of Orthopaedic Surgery (ABOS) Certifying Examination and scores on United States Medical Licensing Examination (USMLE) Steps 1 and 2. METHOD: USMLE Step 1 and Step 2 scores on first attempt were matched with ABOS Part I results for U.S./Canadian graduates taking Part I for the first time between 2002 and 2006. Linear and logistic regression analyses investigated the relationship between ABOS Part I performance and scores on USMLE Step 1 and 2. RESULTS: Step 1 and Step 2 individually each explained 29% of the variation in Part I scores; using both scores increased this percentage to 34%. Results of logistic regression analyses showed a similar, moderately strong relationship with Part I pass/fail outcomes: Examinees with low scores on Steps 1 and 2 were at substantially greater risk for failing Part I. CONCLUSIONS: There is continuing empirical support for use of Step 1 and Step 2 scores in selection of residents to interview for orthopedics residency positions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".